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A Framework in C# for Fingerprint Verification

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In this article, we introduce a framework in C# for fingerprint verification, we briefly explain how to perform fingerprint verification experiments and how to integrate your algorithms to the framework.
Prize winner in Competition "Best C# article of July 2010"

Introduction

Fingerprint recognition [1] is an active research area nowadays. An important component in fingerprint recognition systems is the fingerprint matching algorithm. According to the problem domain, fingerprint matching algorithms are classified in two categories: fingerprint verification algorithms and fingerprint identification algorithms. The aim of fingerprint verification algorithms is to determine whether two fingerprints come from the same finger or not. On the other hand, the fingerprint identification algorithms search a query fingerprint in a database looking for the fingerprints coming from the same finger.

There are hundreds of papers concerning to fingerprint verification but, as far as we know, there is not any framework for fingerprint verification available on the web. So, you must implement your own tools in order to test the performance of your fingerprint verification algorithms. Moreover, you must spend a lot of time implementing algorithms of other authors to compare with your algorithms. This was our motivation to put our fingerprint verification framework available for everyone.

The most closely related work to our framework is the FVC-onGoing web system. This system has the following limitations:

  • You do not have access to any algorithm but yours.
  • It is not a framework; so, you cannot reuse any software component.
  • It cannot be used with educational purpose because students cannot see how the algorithms work.
  • After performing an experiment using a database (standard or hard) you must wait 30 days to perform another experiment using the same database.
  • You do not have control over the fingerprint databases. Thus, you cannot use your own databases neither modify the existing ones.
  • You do not have access to the fingerprints that your algorithm fails matching. Hence, you cannot analyze why the algorithm fails in order to fix your code.
  • You cannot create an experiment with a custom protocol for performance evaluation.

If any of the above mentioned limitations constitute a problem for you, then you should use our framework.

Our framework is implemented in C# with .Net Framework for two main reasons. First, C# has become one of the most popular programming languages. Second, the technologies, tools and class libraries available on .Net Framework save us a lot of time of coding.

Our framework allows performing fingerprint verification experiments for databases B of FVC2000, FVC2002 and FVC2004; and databases A of FVC2002 and FVC2004. In this experiments, we use the performance indicators of the Fingerprint Verification Competitions [2] (EER(%), FMR100(%), FMR1000(%), ZeroFMR(%), Time(ms) and ROC curves). Additionally, you can include experiments with a custom evaluation protocol or different databases.

We implemented the fingerprint verification algorithms proposed by Tico and Kuosmanen [3], Jiang and Yau [4], Medina-Pérez et al. [5, 6, 8, 12], and Qi et al. [9]. It is important to highlight that, despite the algorithm of Qi et al. is a combination of a minutiae matching algorithm with an orientation based algorithm, we implemented only the minutiae matching algorithm. We also implemented the feature extraction algorithms proposed by Ratha et al. [10] and the orientation image extractor proposed by Sherlock et al. [11]. This framework allows you to include new fingerprint matching algorithms as well as new feature extraction algorithms with minimum effort and without recompiling the framework.

One of the goals that we kept in mind while developing this framework was to achieve class interfaces as simple as possible. This way, adding new algorithms is pretty straightforward.

We hope this work motivates you and more people to collaborate with us in order to implement more algorithms.

In this article we briefly explain how:

  • to perform fingerprint verification experiments,
  • to see the matching minutiae after performing an experiment with a minutiae based algorithm,
  • to compute and display features from certain fingerprint,
  • to integrate your algorithms to the framework.

Before using the framework

With this article, we provide the following files:

You can implement custom evaluation protocols and you can work with your own databases. Nevertheless, we implemented evaluation protocols to work with the following databases:

Running a fingerprint verification experiment

Extract the file “FingerprintRecognition.zip” and build the solution. Then you can debug the project “FR.FVCExperimenter” or you can execute “FR.FVCExperimenter.exe” in the directory containing the generated assemblies. The following window will open:

In the “Resources” text box, specify the path of the database that you are going to use, for example: “D:\PR Databases\Fingerprints\FVC2004\DB1_B”. Select the proper experiment type in the combo box with label “Experiment”. Use the combo boxes “Minutia Extractor”, “Orientation Image Extractor” and “Skeleton Image Extractor“ to select the algorithms that will be used to compute the basic features (minutiae, orientation image and skeleton image).

Use the combo box with label “Matcher” to select a fingerprint verification algorithm, and the combo box with label “Feature Provider” to select the algorithm that will be used to store and retrieve the features for the selected matcher. Despite that we implemented only one feature provider for each matcher, there are possible scenarios where you may have multiple feature providers for each matcher.

The grid with label “Properties” allows changing the parameters of any selected algorithm.

Click on “Execute Experiment” button to run the experiment. This experiment uses the evaluation protocol of the Fingerprint Verification Competitions [2]. The performance indicators computed in this experiments are: EER(%), FMR100(%), FMR1000(%), ZeroFMR(%), Time(ms) and ROC curves. These indicators are saved in a file which name is formed by the name of the matching algorithm concatenated with ".Summary.csv". This file is saved in a folder by the name of "Results" in the same folder where the fingerprints are stored. Two more files are saved, one file containing the false matching fingerprints and the other containing the false non matching fingerprints.

If you want to match two fingerprints and verify the coincident minutiae, then click the “Visual Match” button which will open the “Visual Fingerprint Matching” form. Load the fingerprints that you want to compare and click the “Match” button. The feature extractors and matcher selected in the “FVC Experimenter” are also used here to perform the fingerprint matching. The following is an example of matching two fingerprints:

Visualizing features

If you want to visualize features for certain fingerprint then you can use “FR.FeatureDisplay” project. In “Fingerprint Feature Display” form, you can change the feature extractor and feature display. In the framework, we include classes to visualize minutiae, orientation image and skeleton image.

The following is an example visualizing the orientation image of a fingerprint:

Matching fingerprints out of this framework

This section presents an example of using the framework to match two fingerprint images in a custom user application. It includes the three steps to compare two fingerprint images: image loading, feature extraction, and feature comparison. In this case, users need to add references from their application to the assemblies FR.Core and FR.Medina2012. Assemblies SHullDelaunayTriangulation and ImageProcessingTools must be included in the output folder where the binary files appear.

// Loading fingerprints
var fingerprintImg1 = ImageLoader.LoadImage(fileName1);
var fingerprintImg2 = ImageLoader.LoadImage(fileName2);

// Building feature extractor and extracting features
var featExtractor = new MTripletsExtractor(){ MtiaExtractor = new Ratha1995MinutiaeExtractor()};
var features1  =  featExtractor.ExtractFeatures(fingerprintImg1);
var features2  =  featExtractor.ExtractFeatures(fingerprintImg2);

// Building matcher and matching
var matcher = new M3gl();
double similarity = matcher.Match(features1, features2); 

The example, using the M3gl matcher, shows how easy is to use the framework, and how clean and self-explanatory the user code is. The rules for good designing applied in the framework allow the users to replace or change any component with minimal effort. For example, in order to use PN matcher in the above code, users only need to replace MTripletsExtractor with PNFeatureExtractor, M3gl with PN, and reference FR.Medina2012 with FR.Parziale2004.

Adding new algorithms to the framework

The first thing that you need to know is that you do not need to modify the applications of the framework in order to recognize your algorithms because we use Reflection to load all algorithms dynamically at execution time.

You may create as many assemblies as you want in the directory containing the framework. For each new assembly, go to the properties and set the output path with value “..\bin\Release\”.

In order to add a new feature extractor, you must inherit from the generic class FeatureExtractor<T> and implement the method ExtractFeatures(Bitmap image). For example, suppose that you want to create an extractor for features of type MyFeature, then you could implement a class like the following:

public class MyFeatureExtractor : FeatureExtractor<MyFeature>
{
    public override MyFeature ExtractFeatures(Bitmap image)
    {
        // Place here your code to extract features
    }
} 

In case that your new features are built upon some existing features then you can do like follows:

public class MyFeatureExtractor : FeatureExtractor<MyFeature>
{     
    public FeatureExtractor<List<Minutia>> MtiaExtractor { set; get; }

    public FeatureExtractor<OrientationImage> OrImgExtractor { set; get; }

    public override MyFeature ExtractFeatures(Bitmap image)
    {
    try
        {
             var mtiae = MtiaExtractor.ExtractFeatures(image);
             var orImg = OrImgExtractor.ExtractFeatures(image);
             return ExtractFeatures(mtiae, orImg);
        }
        catch (Exception e)
        {
             if (MtiaExtractor == null)
                throw new InvalidOperationException("Cannot extract MyFeature: Unassigned minutia list extractor!", e);
             if (OrImgExtractor == null)
                throw new InvalidOperationException("Cannot extract MyFeature: Unassigned orientation image extractor!", e);
             throw;
        }
    }

    public MyFeature ExtractFeatures(List<Minutia> mtiae, OrientationImage orImg)
    {
        // Place here your code to extract features
    }       
}

For each feature extractor you must create a resource provider. Resource providers allow saving (retrieving) to (from) file the resources associated to fingerprints. The framework includes resource providers for extractors of minutiae (MinutiaListProvider), orientation image (OrientationImageProvider), and skeleton image (SkeletonImageProvider). The following is an example of a resource provider for the feature extractor defined above.

public class MyFeatureProvider : ResourceProvider<MyFeature>
{
    public MinutiaListProvider MtiaListProvider { get; set; }

    public OrientationImageProvider OrImgProvider { get; set; }

    public override string GetSignature()
    {
        return "myf";        
    }

    public override bool IsResourcePersistent()
    {
        return true;
    }

    protected override MyFeature Extract(string fingerprint, ResourceRepository repository)
    {
        try
        {
            var mtiae = MtiaListProvider.GetResource(fingerprint, repository);
            var orImg = OrImgProvider.GetResource(fingerprint, repository);
            return featureExtractor.ExtractFeatures(mtiae, orImg);
        }
        catch (Exception e)
        {
            if (MtiaListProvider == null)
                throw new InvalidOperationException("Unable to extract MyFeature: Unassigned minutia list provider!", e);
            if (OrImgProvider == null)
                throw new InvalidOperationException("Unable to extract MyFeature: Unassigned orientation image provider!", e);
            throw;
        }
    }

    private MyFeatureExtractor featureExtractor = new MyFeatureExtractor();
}

Now, it is time to create a new fingerprint matching algorithm. Suppose that you want to match features of type MyFeature, then you must create a matcher like this:

public class MyMatcher : Matcher<MyFeature>
{        
    public override double Match(MyFeature query, MyFeature template)
    {
        // Place here your code to match fingerprints
    }
}

In case that you are implementing a minutia matching algorithm then you should modify the above code as follows:

public class MyMatcher : Matcher<MyFeature>, IMinutiaMatcher
{
    public override double Match(MyFeature query, MyFeature template)
    {
        List<MinutiaPair> matchingMtiae;
        return Match(query, template, out matchingMtiae);
    }

    public double Match(object query, object template, out List<MinutiaPair> matchingMtiae)
    {
        // Place here your code to match fingerprints
    }
}

Integrating existing algorithms into the framework

The users do not need to modify the framework in order to integrate custom algorithms because Reflection is used to load all the algorithms dynamically at execution time. This way, users should add new algorithms to their own custom assemblies.

In order to use an existing matching algorithm within the framework, the first thing that users need to do is to create a resource provider. Resource providers allow saving (retrieving) to (from) files the resources associated with fingerprints. For example, suppose that users want to integrate SourceAFIS SDK (http://www.sourceafis.org/) into the framework, then the following feature provider can be used:

public class SourceAFISFeatureProvider : ResourceProvider<Person>
{
  protected  override  Person  Extract(string  fingerprint, ResourceRepository repository)
  {
    Fingerprint fp = new  Fingerprint();
    fp.AsBitmap = imageProvider.GetResource(fingerprint, repository); 
    Person person = new Person(); 
    person.Fingerprints.Add(fp); 
    Afis.Extract(person);
    return person;
  }

  public override string GetSignature()
  {
    return string.Format("sAFIS");
  }

  public  override  bool  IsResourcePersistent()
  {
    return true;
  }
  
  private static AfisEngine Afis = new AfisEngine();
} 

Now, the fingerprint matching algorithm can be wrapped within the following class:

public class SourceAFISMatcher : Matcher<Person>
{
  public override double Match(Person query, Person template)
  {
    return Afis.Verify(query, template);
  }
  
  private static AfisEngine Afis = new AfisEngine();
} 

Conclusions

In this article we introduce a framework in C# for fingerprint verification. We briefly explain how to perform fingerprint verification experiments and how to integrate your algorithms to the framework. We provide several matching algorithms and feature extraction algorithms that you can use not only for experimental purpose, but also to create your own applications. We provide the source code of all the algorithms so the user can reuse any part of the code as well as any software component.

Acknowledgment

We would like to thank MSc. Dania Yudith Suárez Abreu for her contribution improving the grammar and style of this paper. Thanks to Jani Giannoudis for suggesting the inclusion of inner exception parameter in the examples.

References

  1. D. Maltoni, D. Maio, A. K. Jain, and S. Prabhakar, "Handbook of fingerprint recognition," Second ed. London: Springer-Verlag, 2009.
  2. R. Cappelli, D. Maio, D. Maltoni, J. L. Wayman, and A. K. Jain, "Performance evaluation of fingerprint verification systems," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, pp. 3-18, 2006.
  3. M. Tico and P. Kuosmanen, "Fingerprint matching using an orientation-based minutia descriptor," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, pp. 1009-1014, 2003.
  4. X. Jiang and W. Y. Yau, "Fingerprint minutiae matching based on the local and global structures," in 15th International Conference on Pattern Recognition, Barcelona, Spain, 2000, pp. 1038-1041.
  5. M. A. Medina-Pérez, A. Gutiérrez-Rodríguez, and M. García-Borroto, "Improving fingerprint matching using an orientation-based minutia descriptor," in 14th Iberoamerican Congress on Pattern Recognition, CIARP 2009, Guadalajara, México, 2009, pp. 121-128.
  6. M. A. Medina-Pérez, M. García-Borroto, A. E. Gutierrez-Rodriguez, L. Altamirano-Robles, "Robust fingerprint verification using m-triplets," in: International Conference on Hand-Based Biometrics (ICHB 2011), Hong Kong, 2011, pp. 1-5. (DOI: 10.1109/ICHB.2011.6094348. E-ISBN: 978-1-4577-0489-5. Print ISBN: 978-1-4577-0491-8).
  7. W. Wang, J. Li, and W. Chen, "Fingerprint minutiae matching based on coordinate system bank and global optimum alignment," in 18th International Conference on Pattern Recognition, 2006, vol. 4, pp. 401-404.
  8. M. A. Medina-Pérez, M. García-Borroto, A. E. Gutierrez-Rodriguez, L. Altamirano-Robles, "Improving the multiple alignments strategy for fingerprint verification," Lecture Notes in Computer Science, vol. 7329, 2012. (Accepted for publication)
  9. J. Qi, S. Yang, and Y. Wang, "Fingerprint matching combining the global orientation field with minutia," Pattern Recognition Letters, vol. 26, pp. 2424-2430, 2005.
  10. N. Ratha, S. Chen, and A. K. Jain, "Adaptive flow orientation-based feature extraction in fingerprint images," Pattern Recognition, vol. 28, pp. 1657-1672, 1995.
  11. B. G. Sherlock, D. M. Monro, and K. Millard, "Fingerprint enhancement by directional Fourier filtering," IEE Proceedings Vision Image and Signal Processing, vol. 141, no. 2, pp. 87-94, 1994.
  12. M. A. Medina-Pérez, M. García-Borroto, A. E. Gutierrez-Rodríguez, and L. Altamirano-Robles, “Improving Fingerprint Verification Using Minutiae Triplets,” Sensors, vol. 12, pp. 3418–3437, 2012.

License

This article, along with any associated source code and files, is licensed under The Code Project Open License (CPOL)

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About the Authors

Octavio Loyola González received the B.S. and M.S. degrees from the University of Ciego de Ávila, Cuba, in 2010 and 2012 respectively. He is a PhD student at Instituto Nacional de Astrofísica, Óptica y Electrónica (México). His research interests include pattern recognition, fingerprint recognition, and data mining in imbalanced databases.
 
Relevant papers:
 
- J.C. Lorenzo,M. Varela, M. Hernández, A. Gutiérrez, A. Pérez, O. Loyola González, “Integrated criteria to identify the best treatment in plant biotechnology experiments”, Acta Physiologia Plantarum, 2013, pp. 1-4.
 
- O. Loyola-González, M. García-Borroto, M. A. Medina-Pérez, J. F. Martínez-Trinidad, J. A. Carrasco-Ochoa, and G. De Ita, “An Empirical Study of Oversampling and Undersampling Methods for LCMine an Emerging Pattern Based Classifier,” in 5th Mexican Conference on Pattern Recognition MCPR2013, Springer LNCS 7914,2013, pp. 264–273.
 
- Loyola González, Octavio; Gutiérrez, Andrés E.; García, Milton. "Inducción de árboles de decisión: Inducción de árboles de decisión basada en índices de validación de clúster", Editorial Académica Española. Academic Publishing GmbH & Co. KG. ISBN: 978-3-659-00468-1. 2012, pp. 1-88.

Miguel Angel Medina Pérez received the B.S. and M.S. degrees from the University of Ciego de Ávila, Cuba, in 2007. He is a PhD student at the National Institute of Astrophysics, Optics and Electronics (México). His research interests include pattern recognition and fingerprint recognition.
 
https://sites.google.com/site/miguelmedinaperez/

Andres Eduardo Gutierrez Rodriguez is graduated from Las Villas Central University, Cuba, in 2006. He received the M.S. degree in 2009 from the University of Ciego de Ávila, Cuba. His research interests are pattern recognition and biometry.
 
Relevant papers:
 
-M. A. Medina-Pérez, A. Gutiérrez-Rodríguez, and M. García-Borroto, "Improving Fingerprint Matching Using an Orientation-Based Minutia Descriptor," Lecture Notes in Computer Science, vol. 5856, pp. 121-128, 2009.
-A. E. Gutierrez-Rodriguez, M. A. Medina-Perez, J. F. Martinez-Trinidad, J. A. Carrasco-Ochoa, and M. Garcia-Borroto, "New Dissimilarity Measures for Ultraviolet Spectra Identification," Lecture Notes in Computer Science, vol. 6256, pp. 220-229, 2010.

Milton García-Borroto is graduated from Las Villas Central University, Cuba, in 2000. He received the M.S. degree in 2007 from the National Institute of Astrophisics, Optics and Electronics, Mexico, where he continues his studies toward a Ph.D. degree. His research interests are pattern recognition and biometry.
 
Relevant papers:
1. M. García-Borroto, J. F. Martinez Trinidad, J. A. Carrasco Ochoa, M. A. Medina-Pérez, and J. Ruiz-Shulcloper. LCMine: An efficient algorithm for mining discriminative regularities and its application in supervised classification. Pattern Recognition vol. 43, pp. 3025-3034, 2010.
2. M. García-Borroto, J. F. Martinez Trinidad, J. A. Carrasco Ochoa. A New Emerging Pattern Mining Algorithm and Its Application in Supervised Classification. M.J. Zaki et al. (Eds.): PAKDD 2010, Part I, Lecture Notes in Artificial Intelligence, vol. 6118, pp. 150–157, 2010.
3. M. A. Medina-Pérez, A. Gutiérrez-Rodríguez, and M. García-Borroto, "Improving Fingerprint Matching Using an Orientation-Based Minutia Descriptor," Lecture Notes in Computer Science, vol. 5856, pp. 121-128, 2009.
4. M. García-Borroto, Y. Villuendas-Rey, J. A. Carrasco-Ochoa, and J. F. Martínez-Trinidad, "Finding Small Consistent Subset for the Nearest Neighbor Classifier Based on Support Graphs," Lecture Notes in Computer Science, vol. 5856, pp. 465-472, 2009.
5. M. García-Borroto, Y. Villuendas-Rey, J. A. Carrasco-Ochoa, and J. F. Martínez-Trinidad, "Using Maximum Similarity Graphs to Edit Nearest Neighbor Classifiers," Lecture Notes in Computer Science, vol. 5856, pp. 489-496, 2009.
6. M. A. Medina-Pérez, M. García-Borroto, and J. Ruiz-Shulcloper, "Object Selection Based on Subclass Error Correcting for ALVOT," Lecture Notes in Computer Science, vol. 4756, pp. 496-505, 2007.

Comments and Discussions

 
AnswerRe: how to create mcc64 and mta PinmemberMiguel Angel Medina Pérez20-Apr-13 15:00 
GeneralMy vote of 5 PinmemberArigbede Moses30-Mar-13 11:42 
GeneralRe: My vote of 5 PinmemberMiguel Angel Medina Pérez30-Mar-13 13:57 
QuestionMatching by retrieving the fingerprint fromthe database PinmemberMember 865730016-Mar-13 3:46 
AnswerRe: Matching by retrieving the fingerprint fromthe database PinmemberMiguel Angel Medina Pérez16-Mar-13 5:42 
GeneralRe: Matching by retrieving the fingerprint fromthe database [modified] PinmemberMember 865730017-Mar-13 7:50 
AnswerRe: Matching by retrieving the fingerprint fromthe database PinmemberMiguel Angel Medina Pérez18-Mar-13 3:13 
QuestionImplementation on Own image Pinmemberrughimire10-Mar-13 1:41 
Dear Authors,
I found the framework is very useful for me. I am trying to develop fingerprint matching application. While going to the Opensource SDK for my upcoming application, i found this and wanted to try on my test images.
I am now ready to code after reading all the documentation. But in my first attempt i get nothing, Can you please help me How can i test with my image except standard Testing Database?
The code i am trying is
 
 
JYFeatureExtractor ext_tpl = new JYFeatureExtractor();
            JYFeatureExtractor ext_query = new JYFeatureExtractor();
            
            Bitmap bmp_tpl = new Bitmap("e:/finger.tpl.jpg");
            Bitmap bmp_query = new Bitmap("e:/finger.jpg");
            
 
            JYFeatures jyFeatures_tpl = ext_tpl.ExtractFeatures(bmp_tpl);
            JYFeatures jyFeatures_query = ext_tpl.ExtractFeatures(bmp_query);
            JY mjy = new JY();
 
            double sim__value = mjy.Match(jyFeatures_query, jyFeatures_tpl);
 
The image i am using is 500dpi image form http://www.biometrika.it/hiscan_simg.html[^] This is the sensor acquired image.
 
I would be thankful of you. I am in urgent need.
Rupak Raj Ghimire
rughimire@gmail.com

AnswerRe: Implementation on Own image PinmemberMiguel Angel Medina Pérez10-Mar-13 2:12 
QuestionReproduce Results PinmemberTiago_Alves5-Mar-13 6:32 
AnswerRe: Reproduce Results PinmemberMiguel Angel Medina Pérez5-Mar-13 6:44 
GeneralMy vote of 5 PinmemberJavier Jiménez20-Feb-13 20:07 
GeneralRe: My vote of 5 PinmemberMiguel Angel Medina Pérez21-Feb-13 6:43 
GeneralMy vote of 5 Pinmembersumar12-Feb-13 2:28 
GeneralRe: My vote of 5 PinmemberMiguel Angel Medina Pérez12-Feb-13 7:46 
GeneralMy vote of 5 PinmemberFred Flams12-Feb-13 2:09 
GeneralRe: My vote of 5 PinmemberMiguel Angel Medina Pérez12-Feb-13 7:45 
GeneralMy vote of 5 PinmemberURVISH_SUTHAR111-Feb-13 22:56 
GeneralRe: My vote of 5 PinmemberMiguel Angel Medina Pérez12-Feb-13 7:42 
GeneralMy vote of 5 Pinmemberravithejag11-Feb-13 17:29 
GeneralRe: My vote of 5 PinmemberMiguel Angel Medina Pérez11-Feb-13 17:50 
Questionquestion PinmemberYasashiku14-Jan-13 22:19 
AnswerRe: question PinmemberMiguel Angel Medina Pérez15-Jan-13 11:03 
NewsRe: question PinmemberMiguel Angel Medina Pérez11-Feb-13 9:41 
Questionquestion PinmemberYasashiku11-Jan-13 18:21 

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